The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Advances in general purpose microprocessors, graphics processors, and related technologies have enabled further advances in computer vision. Today, many applications involve face recognition, which typically includes face tracking. Most prior-art face tracker tracks face using a global approach. Global approach typically uses statistical classification techniques to predict an ROI region to decide whether or not a face appears in this ROI region. It often contains a “last detect”—“predict bigger ROI region”—“current detect” steps. It makes face tracking highly dependent on face detection. Because of this dependency, the prior art global approach has at least two disadvantages. A first disadvantage is, when face rotates, or partial occlusions, tracking often fails due to detection failure. Another disadvantage is the inter-dependency prevents the prior art approaches from taking advantage of performance improvement from parallel and/or multi-core processing.